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Research Paper | Computer Science & Engineering | India | Volume 14 Issue 1, January 2025 | Popularity: 5.5 / 10

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Explainable Artificial Intelligence for Safely Health Care

Dr T. Amitha, P. Shobana, M. Jayashree, R. Rajalakshmi

Abstract: Medical professionals are now able to better diagnose diseases, plan treatments, and keep tabs on their patients thanks to advances in artificial intelligence (AI). By making prompt and precise recommendations based on massive amounts of information, these AI systems can greatly improve healthcare results. But many AI models, especially deep learning ones, are "black box" in nature, which makes it challenging to use them in therapeutic contexts. In order to trust AI systems and make beneficial use of them in patient care, healthcare providers must comprehend how these systems make judgments. This need has prompted the development of the XAI project (explainable artificial intelligence) field objective, the goal of which is to increase the clarity and openness of AI decision - making processes. Ensuring patient safety with AI requires its explainability. Artificial intelligence (AI) decisions in healthcare have the potential to change people's lives forever. Should an AI model misinterpret data or be biased, it may harm patients. By providing an explanation of the elements and data elements that drove an AI system's treatment plan recommendation, XAI products help clinicians make educated choices and avoid mistakes. The use of artificial intelligence in healthcare is highly dependent on trust. Medical professionals may be hesitant to depend on AI systems, particularly in life - or - death scenarios, if they cannot explain their results. By making the AI process more accessible and elucidating its steps, XAI increases confidence by giving medical professionals the tools they need to validate AI - driven suggestions. Open and honest communication fosters trust in AI systems, facilitating their seamless integration into regular clinical practice. The faith and reliance of doctors and nurses in AI systems increases when they comprehend its inner working sand the reasoning behind its suggestions. This, in turn, improves patient outcomes. Meeting healthcare regulatory and ethical requirements also requires explainability. By providing understandable justifications for AI decisions, XAI ensures adherence to these rules. In addition, XAI aids healthcare practitioners in following ethical norms by increasing the transparency of AI systems; this guarantees that AI - driven treatment is impartial, fair, and serves patients' best interests. Keeping prejudices at bay that can cause racial, gender, or socioeconomic biases to manifest is of the utmost importance. There are obstacles to deploying AI in healthcare, despite its benefits. Explanations that are correct and simple to understand are challenging to write due to the intricacy of medical information, which frequently includes high - dimensional and changeable information. Also, AI model explainability and performance aren't always compatible; simpler versions are easier to understand, but they might not be as effective. Protecting patients' confidential it yas they receive explanations is another obstacle, particularly when dealing with personal health information. Moving forward, research in XAI must focus on finding solutions to these problems so that we can build strong and open artificial intelligence (AI) systems that protect patients' privacy and safety without sacrificing either. When it comes to the secure and efficient application of AI in healthcare, explainable AI is a major step forward. Improved patient safety, greater confidence among medical professionals, and adherence to regulatory and ethical norms are all outcomes of XAI 'efforts to make artificial intelligence (AI) systems more accessible and intelligible. Although there are still challenges, such as protecting patient privacy and striking a balance between model performance and explainability, the future of AI in healthcare is dependent on the ongoing development of XAI. As AI increasingly integrates into clinical practice, the ability to articulate and comprehend AI - driven judgments will be crucial for improving patient outcomes and advancing care quality.

Keywords: artificial intelligence in healthcare, explainable AI, patient safety, medical trust, ethical AI

Edition: Volume 14 Issue 1, January 2025

Pages: 1155 - 1160

DOI: https://www.doi.org/10.21275/SR25124103755

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